Aligning Consumers’ and Farmers’ Behaviors Towards Socially Responsible Agriculture: A Canadian Empirical Study
Bibliographic record
Abstract
In contrast with the growing public pressure for sustainable agriculture, most Canadian farmers have not prioritized adopting socially responsible production practices. In this context, empirical analysis of farmers’ responses to public demand has been crucial to assisting the agricultural sector to better cope with a more sensitive market. This thesis contributes to the literature by analyzing farmers’ behaviors towards social license (SL) to operate and policy mechanisms that comply with their major perceptions and goals. Using data from a survey comprising 400 farmers across Canada, we estimate the motivations behind farmers’ preferences for industry level investments. We find that SL is the least preferred option compared to alternate industry-level investments, which confirms that public and private net benefits are not aligned. On the other side of this balance, the growing disconnection between agri-food production and society reinforces the importance of research examining the motivations behind consumers’ purchase behaviors. In fact, evidence about the psychometric factors underlining the heterogeneity among citizen concerns versus consumers’ purchase intentions remains scarce. By employing a Structural Equation Model (SEM), this thesis also aimed to understand the direct and indirect effects between variables driving consumers’ attitudes towards specially labeled meat. Our findings suggest that information and engagement in social media positively impact individuals’ perceptions and concerns for farm animal welfare. Furthermore, individuals having an altruistic and anti-anthropocentric profile are also more oriented towards sustainable and ethical conduct as shoppers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".